Interactions between transcription factors (TFs) and their DNA binding sites are an integral part of regulatory networks within cells. These interactions control critical steps in development and responses to environmental stresses, and their dysfunction can contribute to the progression of various diseases. However, the genomic binding sites and regulatory functions of most of the approximately 2000 described and predicted human TFs are unknown. Prediction of regulatory sites in the genomes of higher eukaryotes is difficult because of the size and complexity of their genomes. Furthermore, even general themes regarding the locations of DNA regulatory elements are still unknown. My lab uses computational and experimental approaches for studying transcriptional regulatory networks, both in model organisms and in the human genome. Experimentally, we are performing high-density oligonucleotide array-based readout of chromatin immunoprecipitation experiments on human and mouse TFs, to identify their in vivo binding sites in a high-throughput manner at much higher resolution than has been permitted using microarrays spotted with PCR products. We are also using improved in vitro protein binding microarray (PBM) technology to characterize the sequence specificity of transcription factors. Computationally, we are predicting candidate cis regulatory elements by integrating mRNA expression data with data on genomic noncoding regions conserved between the mouse and human genomes. We are also developing a rigorous statistical framework for the analysis of binding site clustering, and using it to develop improved algorithms for the prediction of candidate cis regulatory elements. These studies will permit better understanding of the locations and organization of regulatory DNA elements in higher eukaryotic genomes, and will aid in understanding regulatory complexity arising from combinatorial interactions of TFs. Furthermore, the combination of these data with mRNA expression analysis, protein interaction databases and prior genetic and biochemical data in the literature will allow the development of more detailed models of transcriptional regulatory networks in higher eukaryotes. In addition, we will make our data publicly available, so that other researchers may focus their efforts on those genomic regions most likely to contain cis regulatory elements.